04.02. Building Trusted AI: Why Investment Operations Need More Than an LLM

Yeah.

Um, so a, a, a potential example then using the sort of net, net asset value example that you gave, that, that was something I think that you, you started out doing a lot of your, your work with clients was on this, this particular challenge, you know, that, that, um, fund managers need to know, you know, at a moment's notice what the net asset value of their portfolios is.

That entails basically trying to aggregate information from all of these patchwork systems to, to get a value, and there are frequent errors.

That was, that was one of the things that you first tried to address, you know, starting with the, the rule in Luxembourg that, that obliged fund managers there to actually have a proactive approach to this rather than just, you know, getting a slap on the wrist if they were found in a spot check to have, have got it wrong.

So you kind of dived in there and, and, and helped them structure that.

Uh, the next layer on top, you're talking about the kind of now the more agile workflows that you can add once you have that foundation.

So, uh, if we take, for example, a client who's interested in knowing if, if they are particularly exposed to a company, you know, if, if we call them Astro Y or something.

So what, what is our exposure to Astro Y, this, this huge newly listed company? It's looking very volatile.

I'm concerned about its place in our portfolio.

And your... you... your tool can help them interrogate that question and provide an answer to that question without somebody having to go in and, and resolve all of these data challenges, you know, to, to answer this one query.

Is that, is that right?

Yes, it is.

Not only this type of question, but we started with, in term of use case and application, the ability of saying, "I have my data, it's there, it's clean, and I did it with my deterministic flow, so I am-- I can trust the results, and those are reliable." It's, it's not, you know, pure LLM because today you could be tempted to say, "Who care about the entire process that you described for 10 minutes before? Just take a file and, you know, put it in whatever Claude and ask what is my exposure on that." And I think this is why we read now in the press so much that companies and enterprise are, you know, decreasing their use in term of AI internally is because they have been unsatisfied about the results because it was not used correctly.

And, uh, the ability for us of answering questions such as, "What is my exposure on this asset?" Or, "Do I have an anomaly on the evolution of my NAV?" Or, "Do I have a problem in fees calculation?" Or, "Can I run a reconciliation between IBOR, ABOR and, you know, the custodian book record?" Or, so all those different use cases is to ensure that your data is in order without, so the entire process do not leverage uniquely and, and, and in, in, in 100% based on a pure probabilistic approach.

So we do continue to run the full data management piece as I described before.

And then on top of that, you can have indeed the interaction with an agent that you can create or use template agents that we have in our platform to either investigate, detect anomaly or, you know, have this, uh, kind of conversational approach of exploration of your dataset.

But I do believe it's super important to make understand the industry that it needs to be a mix between deterministic workflow and probabilistic, so LLM approach, and using LLM where they are strong and not using LLM for tasks that they are not, you know, normally have been created for those type of tasks.

So everything that is numerical, everything that is actually that require this, uh, scalability and, and, and, and trust in term of the results require this mix between, between the two approach, and require to put a governance around, around the, the agent that you will use to interrogate and, and do this conversational aspect of, of, with, with your dataset, so that you ensure that you can ground and, you know, decrease the level of, of hallucination you may have if you just go wild on, on use of agent.

So, uh, you'll correct me if m- if I'm wrong, but my impression is that you've kind of arrived at this understanding through that exper- that sort of years of experience you had preceding the introduction of gener- generative AI, and then, then what came afterwards.

So you, you know, in, in lots of domains in financial services, we found before gen AI, where we could kind of automate or, or tighten up processes, very manual processes, you know, using traditional software, we, we did that.

Uh, but it reached its limit.

There was still, there was still this threshold beyond which we couldn't, you know, as you say, unstructured data is something that's traditional software just, uh, never really be- was able to navigate.

So you kind of learned the, the benefits of having a, a really tight process for the things that can be manipulated deterministically.

Uh, you saw the, the value that that created.

Then gen AI arrives, and now you have a tool that you can put on top of that, that can kind of work where you need that little bit of extra agility.